arXiv:2505.08784v2 Announce Type: replace-cross Abstract: As machine learning (ML) enters high-stakes domains, trustworthy uncertainty quantification (UQ) is essential for safety. In this paper we introduce PCS-UQ, a framework based on the Predictability, Computability, and Stability (PCS) principles for veridical data science. Starting with a candidate set of models or algorithms, PCS-UQ integrates a rigorous prediction-check to screen out unsuitable models in the set and utilizes bootstrap samples, in order to capture both inter-sample variability and algorithmic instability for the predicti
As AI models are increasingly deployed in critical applications, the demand for robust and trustworthy uncertainty quantification methods is becoming paramount for safety and reliability.
This framework addresses a core challenge in AI trustworthiness, offering a more rigorous approach to validating and understanding the limitations of machine learning models in high-stakes environments.
The explicit integration of predictability, computability, and stability principles provides a systematic method to filter unsuitable models and capture various sources of uncertainty, enhancing the reliability of ML applications.
- · AI Safety Researchers
- · High-Compliance Industries (e.g., healthcare, finance)
- · Trustworthy AI Developers
- · Regulatory Bodies
- · AI Systems Lacking Robust UQ
- · Developers Ignoring Safety Protocols
Increased adoption of explicit uncertainty quantification frameworks in AI development.
Higher barriers to entry for AI models in safety-critical domains without demonstrable UQ.
Shifts in AI certification and auditing standards globally, promoting frameworks like PCS-UQ.
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Read at arXiv cs.LG